Principles, models, and methods for the characterization and analysis of lurkers in online social networks
Roberto Interdonato, Andrea Tagarelli
DIMES Dept., Università della Calabria, Italy
The 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
- Lurking in OSNs: Principles, Models, and Methods
- Lurking in OSNs: Principles, Models, and Methods
Lurk(er): what meanings
Lurking in OSNs: Principles, Models, and Methods
“Lurker”: let’s google it …
Lurking in OSNs: Principles, Models, and Methods
Lurk(er): what meanings
Lurking in OSNs: Principles, Models, and Methods
Outline
2.ꢀ Modeling lurking behaviors
1.ꢀ Lurking in online communities
ꢀ Topology-driven lurking definition
ꢀ The issue of controversial definitions
3.ꢀ Lurker ranking methods
ꢀ Lurking and online behavioral models
4.ꢀ Experimental evaluation
ꢀ Static scenarios
ꢀ The opportunity of de-lurking
ꢀ Dynamic scenarios
5.ꢀ Applications to other domains
ꢀ Vicariously learning ꢀ Lurking in social trust contexts
6.ꢀ Delurking via Targeted Influence
Maximization
ꢀ The DEvOTION algorithm
7.ꢀ Conclusion and future work
Lurking in OSNs: Principles, Models, and Methods
LURKING IN ONLINE COMMUNITIES
Lurking in OSNs: Principles, Models, and Methods
The 1:9:90 rule of participation inequality (1/3)
Arthur, C. (2006). What is the 1% rule? In: The guardian. UK: Guardian News and Media.
Lurking in OSNs: Principles, Models, and Methods
The 1:9:90 rule of participation inequality (2/3)
•ꢀ [Nonnecke & Preece, 2000] Email-based discussion lists:
•ꢀ 77 online health support groups and 21 online technical support groups •ꢀ 46% of the health support group members and 82% of the technical support group members are lurkers
•ꢀ [Swartz, 2006] On Wikipedia: over 50% of all the edits are done by only
0.7% of the users
•ꢀ [van Mierlo, 2014] On four DHSNs (AlcoholHelpCenter,
DepressionCenter, PanicCenter, and StopSmokingCenter):
•ꢀ 63,990 users, 578,349 posts •ꢀ Lurkers account for 1.3% (n=4668), Contributors for 24.0% (n=88,732), and
Superusers for 74.7% (n=276,034) of content
Nonnecke, B., Preece, J. (2000). Lurker Demographics: Counting the Silent. In Proc. SIGCHI Human Factors in Computing. Swartz, A. (2006). Raw thought: Who writes Wikipedia. Blog article at www.aaronsw.com/weblog/whowriteswikipedia. van Mierlo, T. (2014). The 1% rule in four digital health social networks: An observational study. Medical Internet Research, 16(2).
Lurking in OSNs: Principles, Models, and Methods
The 1:9:90 rule of participation inequality (3/3)
•ꢀ Online learning courses:
•ꢀ No relation between interactivity (i.e., posting) and learning (i.e., earned grade) •ꢀ Extend the notion of interactivity to include the lurking activity
•ꢀ Each of the 128 students reads at least one contribution •ꢀ 62% of the class are lurkers—only reading posts, not contributing anything
•ꢀ No correlation between the no. of readers and the no. of writers •ꢀ Every participant, active or lurking, reads more postings than they write
•ꢀ Active participation in an online discussion list, based on passive lurking, is expressed by reading, reflecting on the contribution of all the other members
Ebner, M., Holzinger, A. (2005). Lurking: An underestimated human-computer phenomenon. IEEE Multimedia, 12(4), 70–75.
Lurking in OSNs: Principles, Models, and Methods
Perception of lurking (1/2)
•ꢀ Lurkers as “free-riders” [Kollock & Smith,1996; Morris & Ogan, 1996; Wellman
& Gulia,1999; Rheingold, 2000]
•ꢀ Sustainability of an online community
•ꢀ Fresh content and timely interactions •ꢀ Lurkers contribute little value [van Mierlo, 2014]
•ꢀ Lurkers may impair the virality of the community [Nielsen, 2011]
Kollock, P., Smith, M. (1996). Managing the virtual commons. Computer-mediated communication: Linguistic, social, and cross- cultural perspectives, 109–128.
Morris, M., Ogan, C. (1996). The internet as mass medium. Journal of Communication, 46(1), 39–50. Wellman, B., Gulia, M. (1999). Net surfers don’t ride alone: Virtual communities as communities. Networks in the Global Village, 331– 366.
Rheingold, H. (2000). The virtual community: Homesteading on the electronic frontier. MIT Press.
Nielsen, J. (2011). Participation inequality: Encouraging more users to contribute, http://www.useit.com/alertbox/ participation_inequality.html.
Lurking in OSNs: Principles, Models, and Methods
Perception of lurking (2/2)
•ꢀ Most lurkers are NOT free-riders (e.g., [Nonnecke, Preece, & Andrews, 2004; Nonnecke,
Andrews, & Preece, 2006])
•ꢀ Lurking can be regarded as passive participation that permits inclusion [Ferree, 2002] •ꢀ Lurking is normal and an active, participative and valuable form of online behavior [Edelmann,
2013]
•ꢀ Lurkers perceive themselves as community members [Nonnecke et al., 2006] •ꢀ Lurking as a form of cognitive apprenticeship: “legitimate peripheral participation” [Lave &
Wenger, 1999]
Nonnecke, B., Preece, J., Andrews, D. (2004). What lurkers and posters think of each other. In Proc. the 37th annual Hawaii Int. Conf. on System Sciences. Nonnecke, B., Andrews, D., Preece, J. (2006). Non-public and public online community participation: Needs, attitudes and behavior. Electronic Commerce
Research, 6(1), 7–20. Ferree, M. M., Gamson, W. A., Gerhards, J., Rucht, D. (2002). Shaping abortion discourse: Democracy and the public sphere in Germany and the United
States. New York, Cambridge University Press. Edelmann, N. (2013). Reviewing the definitions of ‘‘Lurkers’’ and some implications for online research. Cyberpsychology, Behavior, and Social Networking, 16(9), 645–649.
Lave, J., Wenger, E. (1999). Legitimate peripheral participation. Learners, learning and assessment. London: The Open University, pp. 83–89.
Lurking in OSNs: Principles, Models, and Methods
How to identify lurkers(1/4)
•ꢀ Two main features: seldom posting, mostly reading contents
•ꢀ Attempts to set quantitative standards:
•ꢀ “never post in an online community” [Nonnecke et al., 2006] •ꢀ “post messages only once in a long while” [Golder & Donath, 2004] •ꢀ “no contribution during a 3-month period” [Nonnecke & Preece, 2000] •ꢀ “#posts<4 from the beginning, or never posted in the last 4 months” [Ganley et al., 2012]
•ꢀ Accounting for the “login” dimension [Chen, 2004]
•ꢀ Lurkers log into the community every week throughout a 6-week timespan
Golder, S. A., Donath, J. (2004). Social roles in electronic communities. Internet Research, 5, 19–22. Ganley, D., Moser, C., Groenewegen, P. (2012). Categorizing behavior in online communities: A look into the world of cake bakers. In Proc. HICSS, pp. 3457–3466. Chen, F. C. (2004). Passive forum behaviors (lurking): A community perspective. In Proc. 6th Int. Conf. on Learning Sciences, pp. 128–135.
Lurking in OSNs: Principles, Models, and Methods
How to identify lurkers(2/4)
•ꢀ Find a certain percentage of most non-active users as lurkers
•ꢀ e.g., [Rau et al., 2008] On Microsoft’s Wallop SNS, 40% of the most non-active as lurkers
•ꢀ Two continuous dimensions (participation pattern) [Leshed, 2005]:
•ꢀ Publicity: ratio of public (i.e., posting) to non-public (i.e., reading) activities •ꢀ Intensity: the frequency of total activities performed by a member
•ꢀ Lurkers tend to have higher intensity and lower publicity
Rau, P.-L. P., Gao, Q., Ding, Y. (2008). Relationship between the level of intimacy and lurking in online social network services. Computers in Human Behavior, 24(6), 2757–2770. Leshed, G. (2005). Posters, lurkers, and in between: A multidimensional model of online community participation patterns. In Proc. HIC.
Lurking in OSNs: Principles, Models, and Methods
How to identify lurkers(3/4)
•ꢀ Lurkers may be classified into: [Takahashi et al. 2003; Walker et al. 2013]
•ꢀ Passive lurkers: only read for their use •ꢀ Active lurkers: for propagation, practical use, or personal contact
•ꢀ Lurkers vs. “non-users” [Springer et al. 2015]
•ꢀ Lurking as passive participation, as opposed to commenting (active participation) •ꢀ Non-users: read news but have no interest in the user comments/discussions
Takahashi, M., Fujimoto, M., Yamasaki, N. (2003). The active lurker: Influence of an in-house online community on its outside environment. In Proc. ACM
SIGGROUP Conf. on Supporting Group Work, pp. 1–10.
Walker, B., Redmond, J., Lengyel, A. (2013). Are they all the same? Lurkers and posters on the net. eCULTURE, 3(1). Springer, N., Engelmann, I., Pfaffinger, C. (2015). User comments: motives and inhibitors to write and read. Information, Communication & Society, 18(7): 798-815
Lurking in OSNs: Principles, Models, and Methods
How to identify lurkers(4/4)
•ꢀ Can we generalize using the previously discussed criteria?
•ꢀ No, it depends on the size, topics and culture of the online community! •ꢀ Many factors influence online behaviors (e.g., [Bishop, 2007; Fan et al., 2009]):
•ꢀ Environmental influences •ꢀ Personal characteristics •ꢀ Organizational commitment
•ꢀ Many lurkers: good or bad?
•ꢀ Active lurkers are beneficial for the propaganda and development of the community •ꢀ but they have low posting rate and lack of valuable content •ꢀ Emergence for strategies to promote de-lurking
Bishop, J. (2007). Increasing participation in online communities: A framework for human–computer interaction. Computers in Human Behavior, 23(4), 1881–1893. Fan, Y.-W., Wu, C.-C., Chiang, L.-C. (2009). Knowledge sharing in virtual community: The comparison between contributors and lurkers. In Proc. Int. Conf. on
Electronic Business, pp. 662–668.
Lurking in OSNs: Principles, Models, and Methods
Lurking and online behavioral models(1/2)
Environmental factors that affect the user’s feeling and the user’s willingness to contribute
Development and spread of community norms, Contribution of valuable resources, and Consumption of resources
Personal characteristics of the users
Factors based on the relationships between the users and the community
Sun, N., Rau, P. P.-L., Ma, L. (2014). Understanding lurkers in online communities: A literature review.
Computers in Human Behavior, 38, 110–117.
Lurking in OSNs: Principles, Models, and Methods
Lurking and online behavioral models(2/2)
Individual factors that influence online behaviors
Sun, N., Rau, P. P.-L., Ma, L. (2014). Understanding lurkers in online communities: A literature review.
Computers in Human Behavior, 38, 110–117.
Lurking in OSNs: Principles, Models, and Methods
Why lurkers lurk (1/4)
•ꢀ Four main motivational factors [Sun et al., 2014]:
1.ꢀ Environmental influence determined by the online community 2.ꢀ Personal preference related to an individual’s personality 3.ꢀ Relationships between the individual and the community 4.ꢀ Security considerations
Sun, N., Rau, P. P.-L., Ma, L. (2014). Understanding lurkers in online communities: A literature review.
Computers in Human Behavior, 38, 110–117.
Lurking in OSNs: Principles, Models, and Methods
Why lurkers lurk (2/4)
1. Environmental influence
•ꢀ Bad usability/interaction design •ꢀ “Too many or too few messages to deal with” •ꢀ Poor quality of the posted contents [Springer et al., 2015]
•ꢀ Negatively influences the affective/entertainment dimension of gratification sought
•ꢀ “Don’t know how to post”
•ꢀ [Nonnecke et al., 2004] Survey of 1188 users from 375 MSN online communities: 7.8% of lurkers
•ꢀ caused by poor usability and insufficient usage guidance
•ꢀ Low response rate and long response delay •ꢀ Low reciprocity
•ꢀ [Fan et al., 2009] Survey with 207 valid responses (74% of lurkers) •ꢀ Leads to think that “posting has no value to me”
•ꢀ “Others respond the way I would” •ꢀ “Just reading/browsing is enough”, “No requirement to post”
•ꢀ [Kucuk, 2010] Survey of 1078 online course students: 31.1% of lurkers
Nonnecke, B., Preece, J., Andrews, D., Voutour, R. (2004). Online lurkers tell why. In Proc. AMCIS. Kücük, M. (2010). Lurking in online asynchronous discussion. Procedia-Social and Behavioral Sciences, 2(2), 2260–2263.
Lurking in OSNs: Principles, Models, and Methods
Why lurkers lurk (3/4)
2. Personal reasons
•ꢀ Introversion, lack of self-efficacy, bashfulness [Nonnecke et al., 2004] •ꢀ Lack of confidence in the ability to post [Lee al., 2006]
•ꢀ 40% of inactive students of an online program [Beaudoin, 2002]
•ꢀ “Don’t feel comfortable writing ideas online”
•ꢀ 25% of inactive students of an online program [Beaudoin, 2002]
•ꢀ No need to post – only seeking for information •ꢀ Nothing to post or lack of expertise •ꢀ “Others had already posted similarly” •ꢀ Time constraints •ꢀ Missing opportunity to earn money (e.g., with commenting activities) [Springer et al., 2015]
Lee, Y.-W., Chen, F.-C., Jiang, H.-M. (2006). Lurking as participation: A community perspective on lurkers’ identity and negotiability. In
Proc. Conf. on Learning Sciences, pp. 404–410.
Beaudoin, M. F. (2002). Learning or lurking?: Tracking the ‘‘invisible’’ online student. The Internet and Higher Education, 5(2), 147–155.
Lurking in OSNs: Principles, Models, and Methods
Why lurkers lurk (4/4)
3. Relationships reasons
•ꢀ Low verbal and affective intimacy with other members
•ꢀ Social penetration theory [Altman & Taylor, 1973]: intimacy develops over time to the extent that members reciprocate disclosures
•ꢀ Lack of commitment to the group •ꢀ Fear making a commitment •ꢀ Don’t want to spend too much time/resources to maintain a commitment
4. Security reasons
•ꢀ Worrying about that posting will violate privacy [Nonnecke & Preece, 2001; Springer et al.,
2015
•ꢀ The community does not satisfy requirements of security and privacy, at different levels
[Wang et al., 2011]
Altman, I., Taylor, D. A. (1973). Social penetration: The development of interpersonal relationships. New York: Holt, Rinehart & Winston. Wang, Y., Norice, G., Cranor, L. F. (2011). Who is concerned about what? A study of American, Chinese and Indian users’ privacy concerns on social network sites. In Proc. Trust and Trustworthy Computing, pp. 146–153. Springer.
Lurking in OSNs: Principles, Models, and Methods
The challenge of “de-lurking”
Provide an environment that makes people’s lives easier
Lurking in OSNs: Principles, Models, and Methods
How to promote de-lurking(1/3)
•ꢀ External stimuli – Social Exchange theory [Thibaut & Kelley, 1959]
•ꢀ Providing rewards and removing negative consequences will strengthen intentions •ꢀ Main actions:
Thibaut, J. W., & Kelley, H. H. (1959). The social psychology of groups (Vol. XIII). Oxford, England: John Wiley.
•ꢀ Tangible or intangible rewards •ꢀ Controlling or informative rewards
•ꢀ Encouragement to participate [Nonnecke et al., 2004; Du, 2006]
•ꢀ Helps to set up a pro-sharing norm •ꢀ Enhances users’ commitment to the community •ꢀ Improves users’ confidence in expressing themselves •ꢀ Make lurkers understand the necessity of their contribution •ꢀ Main actions:
•ꢀ Welcome statements, introduction of reward rules, support for browsing and praise for the moderator
Sun, N., Rau, P. P.-L., Ma, L. (2014). Understanding lurkers in online communities: A literature review.
Computers in Human Behavior, 38, 110–117.
Lurking in OSNs: Principles, Models, and Methods
How to promote de-lurking(2/3)
•ꢀ Guidance for newcomers [Du, 2006]
•ꢀ Newcomers are likely to lurk for a while to learn the culture of the community •ꢀ Help from elder/master users •ꢀ Periodically provide opportunities to join conversations
•ꢀ Usability improvement [Nonnecke et al., 2004, 2006; Du, 2006]
•ꢀ Simplify the procedures to send/respond messages •ꢀ Rearranging the presentation of messages
Sun, N., Rau, P. P.-L., Ma, L. (2014). Understanding lurkers in online communities: A literature review.
Computers in Human Behavior, 38, 110–117.
Lurking in OSNs: Principles, Models, and Methods
How to promote de-lurking(3/3)
•ꢀ Usability improvement [Nazi et al., 2015]
•ꢀ Simplify the task of product/service reviewing •ꢀ Given:
•ꢀ User feedback in textual form •ꢀ A user and an item to review
•ꢀ Goal:
•ꢀ Recommend a set of meaningful terms (i.e., tags) to the user
•ꢀ Method:
•ꢀ Extraction of key tags from available reviews according to:
•ꢀ Relevance, Coverage, and Polarity properties
•ꢀ Formulation of top-k meaningful tags identification
•ꢀ Independent Coverage TagAdvisor •ꢀ Dependent Coverage TagAdvisor
Nazi, A., Das, M., Das, G. (2015). The TagAdvisor: Luring the Lurkers to Review Web Items. In Proc. ACM
SIGMOD 2015, 531–543.
Lurking in OSNs: Principles, Models, and Methods
Lurking as a computational problem(1/2)
•ꢀ Hot topic in social science and computer-human interaction
•ꢀ Lurking conceptualized in relation to cultural capital [Soroka & Rafaeli, 2006], boundary spanning and knowledge brokering activities [Craneeld et al., 2011], group learning [Chen &
Chang, 2011], epistemic curiosity [Schneider et al., 2013]
•ꢀ Focus on the identification of insights that might drive empirical evaluation of lurkers’ traits
•ꢀ Also becoming mature in computer science
•ꢀ Classification methods for actors in an OSN [Fazeen et al., 2011]
•ꢀ including lurkers, although treated marginally
•ꢀ Active and passive lifetime [Lang & Wu, 2013]
•ꢀ the latter however requires to know the user‘s last login date
Soroka, V., Rafaeli, S. (2006). Invisible participants: how cultural capital relates to lurking behavior. In Proc. ACM WWW, pp. 163-172. Chen, F. C., Chang, H. M. (2011). Do lurking learners contribute less?: a knowledge co-construction perspective. In Proc. C&T, pp. 169-178. Craneeld, J., Yoong, P., Hu, S. L. (2011). Beyond Lurking: The Invisible Follower-Feeder In An Online Community Ecosystem. In Proc. PACIS. Schneider, A., von Krogh, G., Jager, P. (2013). “What's coming next?" Epistemic curiosity and lurking behavior in online communities. Computers in Human Behavior 29, 293-303 Fazeen, M., Dantu, R., Guturu, P. (2011) Identication of leaders, lurkers, associates and spammers in a social network: context-dependent and context-independent approaches.
Social Netw. Analys. Mining 1(3), 241-254
Lang, J., Wu, S. F. (2013). Social network user lifetime. Social Netw. Analys. Mining 3(3), 285-297.
Lurking in OSNs: Principles, Models, and Methods
Lurking as a computational problem(2/2)
•ꢀ Emergence for computational models, methodologies, and algorithms for
•ꢀ Understanding lurking behaviors to improve
•ꢀ User modeling, personalization and adaptation
•ꢀ Utilizing the mined knowledge in next-generation
•ꢀ Marketing-oriented applications •ꢀ E-learning platforms •ꢀ Collaborative systems •ꢀ Trust systems
Lurking in OSNs: Principles, Models, and Methods
Next …
Modeling lurking behaviors
Evaluation on Twitter, FriendFeed, Flickr, Google+, and Instagram
•ꢀ Reciprocity, preferential attachment
•ꢀ Delurking-oriented randomization model •ꢀ Percolation/resilience analysis
Lurking over time
Topology-driven definition of lurking In-, Out-, and InOut-neighbors driven ranking
methods
Vicariously Learning on RCNs
•ꢀ Lurkers vs. inactive users , and newcomers •ꢀ Responsiveness
•ꢀ VLRank methods
Lurking and Social Trust
•ꢀ Trust-biased LurkerRank methods
•ꢀ Preferential attachment •ꢀ Temporal trends and clustering •ꢀ Topic evolution
Delurking via Targeted Influence Maximization
•ꢀ The DEvOTION method
Lurking in OSNs: Principles, Models, and Methods
A. Tagarelli, R. Interdonato (2014) Lurking in Social Networks: Topology-based Analysis and Ranking Methods.
Soc. Netw. Analys. Mining (SNAM)
A. Tagarelli, R. Interdonato (2013) “Who’s out there?” Identifying and Ranking Lurkers in Social Networks.
In Pr oc. ASONAM’13
MODELING LURKING BEHAVIORS
•ꢀ In-degree, Out-degree and Lurking •ꢀ Topology-driven Lurking definition